A short-term wind speed prediction method utilizing rolling decomposition and time-series extension to avoid information leakage

References

• Agga, A., A. Abbou, M. Labbadi, and Y. El Houm. 2021. Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models. Renewable Energy 177:101–12. doi:10.1016/j.renene.2021.05.095.
   Web of Science ®Google Scholar
• Bahdanau, D., K. Cho, and Y. Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473. https://arxiv.org/pdf/1409.0473.pdf.
   Google Scholar
• Barbounis, T. G., and J. B. Theocharis. 2007. Locally recurrent neural networks for wind speed prediction using spatial correlation. Information Sciences 177 (24):5775–5797. doi:10.1016/j.ins.2007.05.024.
   Web of Science ®Google Scholar
• Cassola, F., and M. Burlando. 2012. Wind speed and wind energy forecast through Kalman filtering of numerical weather prediction model output. Applied Energy 99:154–66. doi:10.1016/j.apenergy.2012.03.054.
   Web of Science ®Google Scholar
• Cho, K., V. Merrienboer, C. Gulcehre. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1724–1734.
   Google Scholar
• Chung, J., C. Gulcehre, and K. H. Cho. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555. https://arxiv.org/pdf/1412.3555.pdf
   Google Scholar
• Damousis, I. G., M. C. Alexiadis, J. B. Theocharis, and P. S. Dokopoulos. 2004. A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation. IEEE Transactions on Energy Conversion 2004 (2):352–61. doi:10.1109/TEC.2003.821865.
   Google Scholar
• Deng, Y., W. Wei, Q. Chengchun, Z. Wang, and D. Dai. 2001. Boundary-processing-technique in EMD method and Hilbert transform. Chinese Science Bulletin 46 (11):954–60. doi:10.1007/BF02900475.
   Google Scholar
• Denis, E. K., D. Zebre, and J. Ampofo. 2021. A daramola. An assessment of high-resolution wind speeds downscaled with the weather research and forecasting model for coastal areas in Ghana. Heliyon 7 (8):e07768. doi:10.1016/j.heliyon.2021.e07768.
   PubMed Web of Science ®Google Scholar
• Erdem, E., and J. Shi. 2011. ARMA based approaches for forecasting the tuple of wind speed and direction. Applied Energy 88 (4):1405–14. doi:10.1016/j.apenergy.2010.10.031.
   Web of Science ®Google Scholar
• Fukushima, K. 1980. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics 36 (4):193–202. doi:10.1007/BF00344251.
   PubMed Web of Science ®Google Scholar
• Golyandina, N., and A. Zhigljavsky. 2013. Singular spectrum analysis for time series. Berlin, Heidelberg: Springer. doi:10.1007/978-3-642-34913-3.
   Google Scholar
• Han, Y., L. Mi, L. Shen, C. S. Cai, Y. Liu, and K. Li. 2022. A short-term wind speed interval prediction method based on WRF simulation and multivariate line regression for deep learning algorithms. Energy Conversion and Management 2022 (Apr.):258. doi:10.1016/j.enconman.2022.115540.
   Google Scholar
• Huang, C., H. R. Karimi, M. Peng, D. Yang, and Q. Shi. 2023. Evolving long short-term memory neural network for wind speed forecasting. Information Sciences 2023:390–410. doi:10.1016/j.ins.2023.03.031.
   Google Scholar
• Jiang, Z., J. Che, and L. Wang. 2021. Ultra-short-term wind speed forecasting based on EMD-VAR model and spatial correlation. Energy Conversion and Management 250:114919. doi:10.1016/j.enconman.2021.114919.
   Web of Science ®Google Scholar
• Kosana, V., K. Teeparthi, and S. Madasthu. 2022. Hybrid convolutional bi-LSTM autoencoder framework for short-term wind speed prediction. Neural Computing and Applications 34 (15):12653–12662. doi:10.1007/s00521-022-07125-4.
   Web of Science ®Google Scholar
• Krizhevsky, A., I. Sutskever, and G. Hinton. 2012. Efficient and direct estimation of a neural subunit model for sensory coding. Advances in Neural Information Processing Systems 25 (2):3113–21.
   PubMedGoogle Scholar
• Li, K., R. Shen, Z. Wang, B. Yan, Q. Yang, and X. Zhou. 2023. An efficient wind speed prediction method based on a deep neural network without future information leakage. Energy 2023:126589. doi:10.1016/j.energy.2022.126589.
   Google Scholar
• López, G., and P. Arboleya. 2022. Short-term wind speed forecasting over complex terrain using linear regression models and multivariable LSTM and NARX networks in the Andes mountains, Ecuador. Renewable Energy 183:351–68. doi:10.1016/j.renene.2021.10.070.
   Web of Science ®Google Scholar
• Louka, P., G. Galanis, N. Siebert, G. Kariniotakis, P. Katsafados, I. Pytharoulis, and G. Kallos. 2008. Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering. Journal of Wind Engineering & Industrial Aerodynamics 96 (12):2348–62. doi:10.1016/j.jweia.2008.03.013.
   Web of Science ®Google Scholar
• Luo, Y. 2020. Prediction of the wind speed for constructing Keta Railway in the Xinjiang Uygur Autonomous Region of China. Journal of Renewable and Sustainable Energy 12 (2):023702.
   Google Scholar
• Lydia, M., S. Suresh Kumar, A. Immanuel Selvakumar, and G. Edwin Prem Kumar. 2016. Linear and non-linear autoregressive models for short-term wind speed forecasting. Energy Conversion and Management 112:115–24. doi:10.1016/j.enconman.2016.01.007.
   Web of Science ®Google Scholar
• Ning, C. 2017. Ultra-short term wind speed prediction using spatial correlation. Automation of Electric Power Systems. doi:10.7500/AEPS20170109004.
   Google Scholar
• Qian, Z., Y. Pei, H. Zareipour, and N. Chen. 2019. A review and discussion of decomposition-based hybrid models for wind energy forecasting applications. Applied Energy 235 (FEB.1):939–953. doi:10.1016/j.apenergy.2018.10.080.
   Google Scholar
• Sibtain, M., H. Bashir, M. Nawaz, S. Hameed, M. Imran Azam, X. Li, T. Abbas, and S. Saleem. 2022. A multivariate ultra-short-term wind speed forecasting model by employing multistage signal decomposition approaches and a deep learning network. Energy Conversion and Management 2022 (Jul.):263. doi:10.1016/j.enconman.2022.115703.
   Google Scholar
• Uchida, T. 2018. Wind speed and turbulence estimation using large eddy simulation for wind power generation. IEEE Transactions on Sustainable Energy 9 (3):1365–73.
   Google Scholar
• Uender, B. 2021. A survey on Hilbert-huang transform: Evolution, challenges and solutions. Digital Signal Processing 120:103292. doi:10.1016/j.dsp.2021.103292.
   Web of Science ®Google Scholar
• Vautard, R., P. Yiou, and M. Ghil. 1992. Singular-spectrum analysis. Physica D, Nonlinear Phenomena 58 (1–4):95–126. doi:10.1016/0167-2789(92)90103-T.
   Google Scholar
• Wang, Y., and L. Wu. 2016. On practical challenges of decomposition-based hybrid forecasting algorithms for wind speed and solar irradiation. Energy 112:208–220. doi:10.1016/j.energy.2016.06.075.
   Web of Science ®Google Scholar
• Wu, F., and L. Qu. 2008. An improved method for restraining the end effect in empirical mode decomposition and its applications to the fault diagnosis of large rotating machinery. Journal of Sound & Vibration 314 (3–5):586–602. doi:10.1016/j.jsv.2008.01.020.
   Web of Science ®Google Scholar
• Yang, Z., Y. Liu, and Z. Zhang. 2017. Ultra-short-term wind speed prediction with spatial correlation using recent historical observations and PLSR. Power System Technology. doi:10.13335/j.1000-3673.pst.2016.2295.
   PubMedGoogle Scholar
• Yin, D., and Y. Sheng. 2014. Short-term wind speed forecasting based on EMD-RBFNN. Electrical Engineering 42 (11):46–51.
   Google Scholar
• Zarekar, J., G. Payganeh, and M. N. Khajavi. 2020. A new method for reducing end effects in empirical mode decomposition. International Journal of Nonlinear Analysis and Applications. doi:10.22075/IJNAA.2020.4587.
   Web of Science ®Google Scholar
• Zhang, S., Y. Chen, J. Xiao, Z. Fang, Z. Feng, and S.-Z. Yang. 2021. Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with attention mechanism. Renewable Energy 165:174. doi:10.1016/j.renene.2021.04.091.
   Google Scholar
• Zhang, W., Z. Qu, K. Zhang, W. Mao, Y. Ma, and X. Fan. 2017. A combined model based on CEEMDAN and modified flower pollination algorithm for wind speed forecasting. Energy Conversion and Management 136 (MAR):439–51. doi:10.1016/j.enconman.2017.01.022.
   Google Scholar
• Zhang, W., Z. Su, H. Zhang, P. Shi, and Z. Zhang. 2014. Hybrid wind speed forecasting model study based on SSA and intelligent optimized algorithm. Abstract and Applied Analysis 2014:1–14. doi:10.1155/2014/693205.
   Google Scholar
• Zhang, C., H. Wei, J. Zhao, T. Liu, T. Zhu, and K. Zhang. 2016. Short-term wind speed forecasting using empirical mode decomposition and feature selection. Renewable Energy 96:727–737. doi:10.1016/j.renene.2016.05.023.
   Web of Science ®Google Scholar
• Zhao, J., Y. Guo, X. Xiao, J. Wang, D. Chi, and Z. Guo. 2017. Multi-step wind speed and power forecasts based on a WRF simulation and an optimized association method. Applied Energy 197:183–202. doi:10.1016/j.apenergy.2017.04.017.
   Web of Science ®Google Scholar